Do terrorists target urban environments more now than in past days?



Purely descriptive analysis of a change of time in terrorists targeting behavior

• Open Source Database run by University of Maryland
• contains more than 120,000 observations on over 120 variables between 1970 and 2013
• detailed description of target and effect of attack
  • target & weapon,
  • human & property damage
  • perpetrator

The GTD is a powerful database!

Figure 1: attacks per year in all world regions

Figure 2: attacks per year in all world regions, indicating transition of collectors: PGIS - 1997, CETIS - 2008, ISVG - 2011, START since 2011

• improvable data quality
• a lot of information is missing
  
  
  
  The GTD is not providing enough information to answer our research question

• Target's potential urbanity is provided
• Environment's is not
  • Location (suburb? center?)
  • Importance (capital? stronghold?)
  • Urbanity

We use absolute and relative city size (population) as a proxy for urbanity.

• City level data
  • about 45,000 cities from the R 'maps' package
  • about 48,000 cities from Maxmind Inc.
  • additonal 110,000 cities from geonames.com
  • 458 urban centers from wikipedia
• Country level data
  • World Development Indicators from the World Bank
  • Correlates of War from the COW project

• Validity
  • population an insufficient proxy for urbanity
  • but economic, political etc. data not available open source on city level
  • and population probably the single most important one
• Reliability
  • multiple collection sources over decades with unreproducible methods in unclear administrative environments

Within single and across datasets

• Adjusting City and Country Names

  X<-gsub("^Teberan","Tehran", X, ignore.case=TRUE)
  X<-gsub("^Teheran","Tehran", X, ignore.case=TRUE)
  X<-gsub("^Teherau","Tehran", X, ignore.case=TRUE)
  X<-gsub("^Tehram","Tehran", X, ignore.case=TRUE)

• "Reassigning" Cities to Countries

  GTD$country_txt[GTD$country_txt == "Soviet Union" &
                  GTD$city =="Osh" ] <- "Kyrgyzstan"
  GTD$country_txt[GTD$country_txt == "Soviet Union" &
                  GTD$city =="Riga" ] <- "Latvia"
  GTD$country_txt[GTD$country_txt == "Yugoslavia" &
                  GTD$city =="Bogicevica" ] <- "Albania"
  GTD$country_txt[GTD$country_txt == "Yugoslavia" &
                  GTD$city =="Banjica" ] <- "Serbia"

• Geo Referencing absolute & relative city position
• Proximity Referencing each city by assigning it to its closest Urban Center
• Establishing Relative City Size as a share of each countries' largest cityies'total population
• Estimating Past Populations using country level data and growth rates
• Determining Cities' Rank Size given by the population on a country, regional, and world level
• Including additional Control Variables for capitals, largest cities, inter-state war, intra-state war, extra-in war, extra-out war, and coastal megacities

We merge city level, country level, and GTD data together to a reproducible database containing 114086 observations on 44 variables.

• Merged database contains NA share of 11%
• Considered acceptable
• Potential for further reduction
• We assume systematic sources

We use a simple linear regression, depending on Relative City Sizes attacked over time for various world regions. We control for

• Extra-In State War
• Extra-Out State War
• Intra State War
• Inter State War
• Capitals
• Target's Potential Urbanity
• including weightings for human and property damage

Regresison Table

• There is no general global trend for terrorists moving to urban targets
• Results vary across world regions:
  • there is a trend in the Arab World
  • there is a trend in Southeast Asia
• Civil War changes everything!

• Still a lot of potential for further cleaning
• More possibilities for including control variables
• Currently only few projects using GTD for analysis
• Our approach seems promising for more research endavours

• First, the Master's Thesis!